B4J Code Snippet [PyBridge] Non-linear regression with SciPy

New implementation similar to: https://www.b4x.com/android/forum/threads/playing-with-linear-regression.110759/#content
Using SciPy (https://www.b4x.com/android/forum/t...imum-of-a-function-with-scipy.166881/#content) to find the best fit function parameters.

It is based on scipy.optimize.curve_fit which takes as an input a function and X and Y values, and tries to find the parameters that minimize the error.

Functions are defined with Py.Lambda:

functions.Add(CreateMyFunction(Py.Lambda("x, a, b: a * x + b"), xui.Color_Blue)) 'linear function
functions.Add(CreateMyFunction(Py.Lambda("x, a, b, c: a * x ** 2 + b * x + c"), xui.Color_Green)) '2nd degree polynom
functions.Add(CreateMyFunction(Py.Lambda("x, a, b, c, d: a * x ** 3 + b * x ** 2 + c * x + d"), xui.Color_Gray)) '3rd degree polynom

Adding support for serialization of numpy.ndarrays:

Dim converters As PyWrapper = Py.Bridge.GetField("comm").GetField("serializator").GetField("converters")
converters.Set(np.GetField("ndarray"), Py.Lambda("arr: arr.tolist()"))

Note that the functions aren't limited to polynomial functions.